1. Technical Field
The present disclosure relates to registration of medical images and, more specifically, to registration of medical images using learning-based matching functions.
2. Discussion of Related Art
Medical images are images of a human subject that are analyzed for the purposes of diagnosing and treating disease, injury and birth defects. While medical images may be captured using conventional photography, more commonly, medical images involve modalities that are able to image the internal structure of the subject in a non-invasive manner. Examples of such modalities include computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound, fluoroscopy, conventional x-rays, and the like. Medical images may be analogue or digital, two-dimensional or three-dimensional; however, three-dimensional modalities are digital.
Traditionally, acquired medical images are meticulously inspected by a trained medical practitioner, for example a radiologist, to detect instances of abnormality that may be indicative of disease. When disease is found, the radiologist may be able to gain additional diagnostic information from the image, such as a characterization of a size and shape of a suspicious lesion. Additionally, the medical images may be used to accurately locate a lesion so that treatments such as chemotherapy and radiotherapy may be precisely delivered and surgery may be effectively planned.
Where the medical images are three-dimensional, the practitioner may step through a sequence of two-dimensional image slices at regular intervals, inspecting each slice. Thus, inspection of medical images may be tedious and prone to error.
Accordingly, methods of computer aided detection (CAD) have been developed for the automatic location, characterization, and segmentation of suspicious lesions within the human subject. For example, CAD may be used to detect regions that may be tumors. CAD may also be used to locate, characterize and segment anatomical structures. For example, a ribcage may be identified within a medical image.
CAD may either be fully automatic or user assisted. In user assisted CAD, a user may identify certain key structures within the medical image or may otherwise modify search parameters until the user is satisfied with the computerized identification. In fully automatic CAD, user assistance is not required.
Image registration is one of the key technologies of various CAD systems. Registration methods aim to find the optimal transformation between a pair of images. Mathematically, it can be formulated as an optimization problem as Eq. (1).
                              max          T                ⁢                  S          ⁡                      (                                          I                1                            ,                              T                ⁡                                  (                                      I                    2                                    )                                                      )                                              (        1        )            Here, I1 and I2 denote the image pair understudy. S denotes the matching function. T denotes the transformation, which is the variable to be optimized.
Using image registration methods, medical images of different patients or medial images of the same patient taken at different time points can be brought to a canonical space, where the same coordinate corresponds to the same anatomical structures. Image registration plays a fundamental role in populational and longitudinal studies of anatomy and pathology. For example, by registering two medical images of the same patient taking at two different time points, the changes in size, shape, position and function of various anatomical structures between the two time points are revealed. In this way, the progress of disease and/or effectiveness of treatment may be effectively monitored over time.
The effectiveness of registration methods, however, depends greatly on using the proper matching function/similarity measurement. Accordingly, except where otherwise indicated, as used herein, “matching function” and “similarity measurement” share the same meaning.
In recent decades, researchers have proposed multiple matching functions, including cross-correlation, entropy of the difference image and mutual information, etc. However, according to comparison studies, none of these methods is superior to others across different registration problems. Accordingly, there is presently no universally “good” matching unction for medical image registration, and it is unlikely that such a matching function may ever be developed. For any specific application, matching function must be chose to adapt to the nature of the images. Additionally, it is possible that none of the available matching functions is adequate.
Manual design selection of a matching function may involve extensive human involvement, which increase the development cost for registration methods. The high empirical nature of manually designed/selected matching functions might be far from the optimal matching function. Moreover, the scalability of a registration method with a manual designed/selected matching function will be very limited. In an even worse scenario, if the optimal matching function is spatially adaptive, it is very difficult, if not impossible, to manually design/select a set of matching functions.